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Using model trees for classification

Abstract
Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5’, based on Quinlan’s M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
Type
Working Paper
Type of thesis
Series
Computer Science Working Papers
Citation
Frank, E., Wang, Y., Inglis, S., Holmes, G. & Witten, I.H. (1997). Using model trees for classification. (Working paper 97/12). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Date
1997-04
Publisher
Degree
Supervisors
Rights